Artificial intelligence (AI) is at a crucial juncture. With the rapid rise of generative AI, predictions about its impact on business and society are everywhere. However, one critical issue may hinder your organization's ability to fully harness AI's potential trust.
More than half of consumers believe AI poses a serious societal threat. Without trust in AI, both within your organization and among your customers, scaling AI effectively becomes an uphill battle.
The key to solving this problem is building "trusted AI"—AI that’s designed, developed, and governed to meet the needs of diverse stakeholders. This approach ensures accountability, competence, consistency, dependability, empathy, integrity, and transparency. Implementing trusted AI is a strategic way for businesses to leverage the benefits of AI while minimizing risks and concerns.
The AI Trust Gap
Trust in AI refers to confidence in its outcomes. Both developers and consumers need assurance that the AI systems they interact with produce accurate, unbiased, and valuable results. Inaccuracies, unexpected results (like hallucinations from generative AI), and embedded biases are major reasons business executives remain hesitant to trust AI.
A recent Forrester survey revealed that 25% of data and analytics leaders cite trust as a major concern in using AI, while 21% are troubled by the lack of transparency in AI systems. Among consumers, the trust deficit is even more pronounced. Only 28% of U.S. adults trust companies using AI, and 52% believe AI poses a serious threat to society.
For businesses that view AI as a growth driver, bridging this trust gap is essential.
Building Trusted AI
Despite market skepticism, slowing down AI initiatives would be a costly mistake. AI’s vast potential demands not less adoption but the deployment of more trusted AI.
Trusted AI relies on "seven levers of trust" to ensure its credibility:
- Transparency: AI often feels like a "black box." Explaining how models work and adopting explainable AI approaches improve transparency.
- Competence: AI is probabilistic by nature. Business leaders need to accept that AI predictions aren’t deterministic but reflect real-world uncertainties.
- Consistency: As models evolve over time, their performance can fluctuate. Using ModelOps—tools and practices for deploying, monitoring, and retraining models—helps ensure consistency.
- Accountability: AI won’t always be flawless. When errors occur, businesses must take responsibility, explain what happened, and prevent future mistakes.
- Integrity: Assigning responsibility for AI ethics, such as appointing a chief trust officer, ensures your AI processes maintain integrity.
- Dependability: Trust in AI results from its dependability. Simulating scenarios before real-world deployment enhances confidence in AI.
- Empathy: Engaging diverse stakeholders in testing can reduce bias and bring empathy into AI models.
Which trust levers your company emphasizes will depend on your industry and goals. Ultimately, long-term success with AI won’t be determined by how many tools you use or how fast you deploy them. It will be measured by how much trust your employees and customers place in the technology.
